Token Classification
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use dslim/bert-base-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-base-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-base-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -3
config.json
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"do_sample": false,
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"eos_token_ids": 0,
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"finetuning_task": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"type_vocab_size": 2,
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"use_bfloat16": false,
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"vocab_size": 28996
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}
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"do_sample": false,
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"finetuning_task": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"type_vocab_size": 2,
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"use_bfloat16": false,
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"vocab_size": 28996
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}
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